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» Using classifier ensembles to label spatially disjoint data
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77
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INFFUS
2008
97views more  INFFUS 2008»
14 years 10 months ago
Using classifier ensembles to label spatially disjoint data
act 11 We describe an ensemble approach to learning from arbitrarily partitioned data. The partitioning comes from the distributed process12 ing requirements of a large scale simul...
Larry Shoemaker, Robert E. Banfield, Lawrence O. H...
103
Voted
MCS
2005
Springer
15 years 3 months ago
Ensembles of Classifiers from Spatially Disjoint Data
We describe an ensemble learning approach that accurately learns from data that has been partitioned according to the arbitrary spatial requirements of a large-scale simulation whe...
Robert E. Banfield, Lawrence O. Hall, Kevin W. Bow...
KDD
2001
ACM
216views Data Mining» more  KDD 2001»
15 years 10 months ago
The distributed boosting algorithm
In this paper, we propose a general framework for distributed boosting intended for efficient integrating specialized classifiers learned over very large and distributed homogeneo...
Aleksandar Lazarevic, Zoran Obradovic
81
Voted
PCM
2007
Springer
114views Multimedia» more  PCM 2007»
15 years 4 months ago
Random Convolution Ensembles
A novel method for creating diverse ensembles of image classifiers is proposed. The idea is that, for each base image classifier in the ensemble, a random image transformation is g...
Michael Mayo
86
Voted
SDM
2010
SIAM
195views Data Mining» more  SDM 2010»
14 years 11 months ago
Adaptive Informative Sampling for Active Learning
Many approaches to active learning involve periodically training one classifier and choosing data points with the lowest confidence. An alternative approach is to periodically cho...
Zhenyu Lu, Xindong Wu, Josh Bongard